Sentence Similarity
sentence-transformers
Safetensors
Turkish
xlm-roberta
turkish
turkce
academic
retrieval
feature-extraction
text-embeddings-inference
Instructions to use hakansabunis/trakad-embed-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hakansabunis/trakad-embed-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hakansabunis/trakad-embed-v2") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
trakad-embed-v2 — Turkish academic SimCSE fine-tune (633K theses, MultipleNegativesRankingLoss + subject-aware hard negatives)
f1a23a6 verified - Xet hash:
- 53d69b219591ee86d7cfab4b9837f0ddf00cce7d2beb2a7194e14bd4827a87a0
- Size of remote file:
- 16.8 MB
- SHA256:
- 7ef4dd5924152f80fdfe441d154ab14d6d04e5ae9f0c52113b4d4412d681c800
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